Application of LANDSAT data to agricultural resource problems with emphasis on the North American Great Plains

There are no author-identified significant results in this report

Mapping Europe into local climate zones

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives

Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWetland is one of the most valuable ecological systems in nature. Wetland habitat is a set of comprehensive information of wetland distribution, wetland habitat types are essential to wetland management programs. Maps of wetland should provide sufficient detail, retain an appropriate scale and be useful for further mapping and inventory work (Queensland wetland framework). Remotely sensed image classification techniques are useful to detect vegetation patterns and species combination in the inaccessible regions. Automated classification procedures are conducted to save the time of the research. The purpose of the research was to develop a hierarchical classification approach that effectively integrate ancillary information into the classification process and combines ISODATA (iterative self-organizing data analysis techniques algorithm) clustering, Maximum likelihood and rule-based classifier. The main goal was to find out the best possible combination or sequence of classifiers for typically classifying wetland habitat types yields higher accuracy than the existing classified wetland map from Landsat ETM data. Three classification schemes were introduced to delineate the wetland habitat types in the idea of comparison among the methods. The results showed the low accuracy of different classification schemes revealing the fact that image classification is still on the way toward a fine proper procedure to get high accuracy result with limited effort to make the investigation on sites. Even though the motivation of the research was to apply an appropriate procedure with acceptable accuracy of classified map image, the results did not achieve a higher accuracy on knowledge-based classification method as it was expected. The possible reasons are the limitation of the image resolution, the ground truth data requirements, and the difficulties of building the rules based on the spectral characteristics of the objects which contain high mix of spectral similarities

Spatial and Temporal Land Cover Changes in the Simen Mountains National Park, a World Heritage Site in Northwestern Ethiopia

The trend of land cover (LC) and land cover change (LCC), both in time and space, was investigated at the Simen Mountains National Park (SMNP), a World Heritage Site located in northern Ethiopia, between 1984 and 2003 using Geographical Information System (GIS) and remote sensing (RS). The objective of the study was to generate spatially and temporally quantified information on land cover dynamics, providing the basis for policy/decision makers and resource managers to facilitate biodiversity conservation, including wild animals. Two satellite images (Landsat TM of 1984 and Landsat ETM+ of 2003) were acquired and supervised classification was used to categorize LC types. Ground Control Points were obtained in field condition for georeferencing and accuracy assessment. The results showed an increase in the areas of pure forest (Erica species dominated) and shrubland but a decrease in the area of agricultural land over the 20 years. The overall accuracy and the Kappa value of classification results were 88 and 85%, respectively. The spatial setting of the LC classes was heterogeneous and resulted from the biophysical nature of SMNP and anthropogenic activities. Further studies are suggested to evaluate the existing LC and LCC in connection with wildlife habitat, conservation and management of SMNP

Outlining where humans live -- The World Settlement Footprint 2015

Human settlements are the cause and consequence of most environmental and societal changes on Earth; however, their location and extent is still under debate. We provide here a new 10m resolution (0.32 arc sec) global map of human settlements on Earth for the year 2015, namely the World Settlement Footprint 2015 (WSF2015). The raster dataset has been generated by means of an advanced classification system which, for the first time, jointly exploits open-and-free optical and radar satellite imagery. The WSF2015 has been validated against 900,000 samples labelled by crowdsourcing photointerpretation of very high resolution Google Earth imagery and outperforms all other similar existing layers; in particular, it considerably improves the detection of very small settlements in rural regions and better outlines scattered suburban areas. The dataset can be used at any scale of observation in support to all applications requiring detailed and accurate information on human presence (e.g., socioeconomic development, population distribution, risks assessment, etc.)

Global Human Settlement Analysis for Disaster Risk Reduction

The Global Human Settlement Layer (GHSL) is supported by the European Commission, Joint Research Center (JRC) in the frame of his institutional research activities. Scope of GHSL is developing, testing and applying the technologies and analysis methods integrated in the JRC Global Human Settlement analysis platform for applications in support to global disaster risk reduction initiatives (DRR) and regional analysis in the frame of the European Cohesion policy. GHSL analysis platform uses geo-spatial data, primarily remotely sensed and population. GHSL also cooperates with the Group on Earth Observation on SB-04-Global Urban Observation and Information, and various international partners andWorld Bank and United Nations agencies. Some preliminary results integrating global human settlement information extracted from Landsat data records of the last 40 years and population data are presented.JRC.G.2-Global security and crisis managemen

A high resolution spatial population database of Somalia for disease risk mapping

The article investigates the possibility of creating a data collection system in an unstable environment like Somalia to estimate the incidence of infectious diseases in order to improve the reconstruction of the health sector.Maqaalku wuxuu baarayaa sidii lagu samayn lahaa nidaam lagu ururiyo daatooyinka meel aan xasillooneen sida Soomaaliya, si loo qiyaaso saamaynta cudurrada laysu gudbiyo, loona hagaajiyo qaybta caafimaadka.L'articolo indaga sulla possibilità di creare un sistema di raccolta dati in un contesto instabile come quello somalo per stimare l'incidenza di malattie infettive al fine di una migliore ricostruzione del settore sanitario

CIRSS vertical data integration, San Bernardino study

The creation and use of a vertically integrated data base, including LANDSAT data, for local planning purposes in a portion of San Bernardino County, California are described. The project illustrates that a vertically integrated approach can benefit local users, can be used to identify and rectify discrepancies in various data sources, and that the LANDSAT component can be effectively used to identify change, perform initial capability/suitability modeling, update existing data, and refine existing data in a geographic information system. Local analyses were developed which produced data of value to planners in the San Bernardino County Planning Department and the San Bernardino National Forest staff

Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version